Systems and methods for curb detection and pedestrian hazard assessment

ABSTRACT

A detection system for a vehicle is provided. The detection system may include at least one image capture device configured to acquire a plurality of images of an area forward of the vehicle, the area including a curb separating a road surface from an off-road surface and a data interface. The detection system may also include at least one processing device programmed to receive the plurality of images via the data interface, and determine a plurality of curb edge line candidates in the plurality of images. The at least one processing device may be further programmed to identify at least one edge line candidate as an edge line of the curb.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/253,824, filed Aug. 31, 2016 (now allowed), which is a continuationof U.S. patent application Ser. No. 14/712,580, filed on May 14, 2015(now U.S. Pat. No. 9,443,163), which claims the benefit of priority ofU.S. Provisional Patent Application No. 61/993,050, filed on May 14,2014. The foregoing applications are incorporated herein by reference intheir entirety.

BACKGROUND

I. Technical Field

The present disclosure relates generally to autonomous vehiclenavigation and, more specifically, to systems and methods that usecameras to detect a curb associated with a roadway and assess potentialpedestrian hazards.

II. Background Information

As technology continues to advance, the goal of a fully autonomousvehicle that is capable of navigating on roadways is on the horizon.Primarily, an autonomous vehicle may be able to identify its environmentand navigate without input from a human operator. Autonomous vehiclesmay also take into account a variety of factors and make appropriatedecisions based on those factors to safely and accurately reach anintended destination. For example, various objects—such as othervehicles and pedestrians—are encountered when a vehicle typicallytravels a roadway. Autonomous driving systems may recognize theseobjects in a vehicle's environment and take appropriate and timelyaction to avoid collisions. Additionally, autonomous driving systems mayidentify other indicators—such as traffic signals, traffic signs, andlane markings—that regulate vehicle movement (e.g., when the vehiclemust stop and may go, a speed at which the vehicle must not exceed,where the vehicle must be positioned on the roadway, etc.). Autonomousdriving systems may need to determine when a vehicle should changelanes, turn at intersections, change roadways, etc. As is evident fromthese examples, many factors may need to be addressed in order toprovide an autonomous vehicle that is capable of navigating safely andaccurately.

SUMMARY

Embodiments consistent with the present disclosure provide systems andmethods for autonomous vehicle navigation. The disclosed embodiments mayuse cameras to provide autonomous vehicle navigation features. Forexample, consistent with the disclosed embodiments, the disclosedsystems may include one, two, or more cameras that monitor theenvironment of a vehicle and cause a navigational response based on ananalysis of images captured by one or more of the cameras.

Consistent with a disclosed embodiment, a detection system for a vehicleis provided. The detection system may include at least one image capturedevice programmed to acquire a plurality of images of an area forward ofthe vehicle, the area including a curb separating a road surface from anoff-road surface and a data interface. The detection system may alsoinclude at least one processing device configured to receive theplurality of images via the data interface, and determine a plurality ofcurb edge line candidates in the plurality of images. The at least oneprocessing device may be further programmed to identify at least oneedge line candidate as an edge line of the curb.

Consistent with another embodiment, a vehicle is provided. The vehiclemay include a body, at least one image capture device configured toacquire a plurality of images of an area forward of the vehicle, thearea including a curb separating a road surface from an off-roadsurface, and a data interface. The vehicle may also include at least oneprocessing device programmed to receive the plurality of images via thedata interface, and determine a plurality of curb edge line candidatesin the plurality of images. The at least one processing device may befurther programmed to identify at least one curb edge line candidate asan edge line of the curb, and identify regions in the plurality ofimages that correspond to the road surface and the off-road surfacebased on the at least one curb edge line.

Consistent with another disclosed embodiment, a method for curbdetection is disclosed. The method may include acquiring, via at leastone image capture device, a plurality of images of an area forward of avehicle, the area including a curb separating a road surface from anoff-road surface, and determining a plurality of curb edge linecandidates in the plurality of images. The method may also includedetermining a height of a step function associated with each curb edgeline candidate, and identifying at least one curb edge line candidate asan edge line of the curb based on the determined heights.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processing device and perform any of themethods described herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 2D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicleincluding a rearview mirror and a user interface for a vehicle imagingsystem consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from adifferent perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to storeinstructions for performing one or more operations consistent with thedisclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one ormore navigational responses based on monocular image analysis consistentwith disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one ormore vehicles and/or pedestrians in a set of images consistent with thedisclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting roadmarks and/or lane geometry information in a set of images consistentwith the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detectingtraffic lights in a set of images consistent with the disclosedembodiments.

FIG. 5E is a flowchart showing an exemplary process for causing one ormore navigational responses based on a vehicle path consistent with thedisclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determiningwhether a leading vehicle is changing lanes consistent with thedisclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one ormore navigational responses based on stereo image analysis consistentwith the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one ormore navigational responses based on an analysis of three sets of imagesconsistent with the disclosed embodiments.

FIG. 8 is an block diagram of an exemplary memory configured to storeinstructions for performing one or more operations consistent withdisclosed embodiments.

FIG. 9 is an illustration of an image of an environment of a vehicleconsistent with disclosed embodiments.

FIGS. 10A and 10B are illustrations of images that include a curbconsistent with disclosed embodiments.

FIG. 11 is an illustration of an exemplary modeled curb consistent withdisclosed embodiments.

FIG. 12 is a flowchart of an exemplary process for identifying a curbwithin an image or video sequence consistent with disclosed embodiments.

FIG. 13 is a flowchart of an exemplary process for assessing hazardspresented by pedestrians consistent with disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

FIG. 1 is a block diagram representation of a system 100 consistent withthe exemplary disclosed embodiments. System 100 may include variouscomponents depending on the requirements of a particular implementation.In some embodiments, system 100 may include a processing unit 110, animage acquisition unit 120, a position sensor 130, one or more memoryunits 140, 150, a map database 160, and a user interface 170. Processingunit 110 may include one or more processing devices. In someembodiments, processing unit 110 may include an applications processor180, an image processor 190, or any other suitable processing device.Similarly, image acquisition unit 120 may include any number of imageacquisition devices and components depending on the requirements of aparticular application. In some embodiments, image acquisition unit 120may include one or more image capture devices (e.g., cameras), such asimage capture device 122, image capture device 124, and image capturedevice 126. System 100 may also include a data interface 128communicatively connecting processing device 110 to image acquisitiondevice 120. For example, data interface 128 may include any wired and/orwireless link or links for transmitting image data acquired by imageaccusation device 120 to processing unit 110.

Both applications processor 180 and image processor 190 may includevarious types of processing devices. For example, either or both ofapplications processor 180 and image processor 190 may include amicroprocessor, preprocessors (such as an image preprocessor), graphicsprocessors, a central processing unit (CPU), support circuits, digitalsignal processors, integrated circuits, memory, or any other types ofdevices suitable for running applications and for image processing andanalysis. In some embodiments, applications processor 180 and/or imageprocessor 190 may include any type of single or multi-core processor,mobile device microcontroller, central processing unit, etc. Variousprocessing devices may be used, including, for example, processorsavailable from manufacturers such as Intel®, AMD®, etc. and may includevarious architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor 180 and/or image processor190 may include any of the EyeQ series of processor chips available fromMobileye®. These processor designs each include multiple processingunits with local memory and instruction sets. Such processors mayinclude video inputs for receiving image data from multiple imagesensors and may also include video out capabilities. In one example, theEyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2®architecture consists of two floating point, hyper-thread 32-bit RISCCPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), threeVector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller,128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bitVideo output controllers, 16 channels DMA and several peripherals. TheMIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the secondMIPS34K CPU and the multi-channel DMA as well as the other peripherals.The five VCEs, three VMP® and the MIPS34K CPU can perform intensivevision computations required by multi-function bundle applications. Inanother example, the EyeQ3®, which is a third generation processor andis six times more powerful that the EyeQ2®, may be used in the disclosedembodiments.

Any of the processing devices disclosed herein may be configured toperform certain functions. Configuring a processing device, such as anyof the described EyeQ processors or other controller or microprocessor,to perform certain functions may include programming of computerexecutable instructions and making those instructions available to theprocessing device for execution during operation of the processingdevice. In some embodiments, configuring a processing device may includeprogramming the processing device directly with architecturalinstructions. In other embodiments, configuring a processing device mayinclude storing executable instructions on a memory that is accessibleto the processing device during operation. For example, the processingdevice may access the memory to obtain and execute the storedinstructions during operation.

While FIG. 1 depicts two separate processing devices included inprocessing unit 110, more or fewer processing devices may be used. Forexample, in some embodiments, a single processing device may be used toaccomplish the tasks of applications processor 180 and image processor190. In other embodiments, these tasks may be performed by more than twoprocessing devices.

Processing unit 110 may comprise various types of devices. For example,processing unit 110 may include various devices, such as a controller,an image preprocessor, a central processing unit (CPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices for image processing and analysis. The imagepreprocessor may include a video processor for capturing, digitizing andprocessing the imagery from the image sensors. The CPU may comprise anynumber of microcontrollers or microprocessors. The support circuits maybe any number of circuits generally well known in the art, includingcache, power supply, clock and input-output circuits. The memory maystore software that, when executed by the processor, controls theoperation of the system. The memory may include databases and imageprocessing software. The memory may comprise any number of random accessmemories, read only memories, flash memories, disk drives, opticalstorage, tape storage, removable storage and other types of storage. Inone instance, the memory may be separate from the processing unit 110.In another instance, the memory may be integrated into the processingunit 110.

Each memory 140, 150 may include software instructions that whenexecuted by a processor (e.g., applications processor 180 and/or imageprocessor 190), may control operation of various aspects of system 100.These memory units may include various databases and image processingsoftware. The memory units may include random access memory, read onlymemory, flash memory, disk drives, optical storage, tape storage,removable storage and/or any other types of storage. In someembodiments, memory units 140, 150 may be separate from the applicationsprocessor 180 and/or image processor 190. In other embodiments, thesememory units may be integrated into applications processor 180 and/orimage processor 190.

Position sensor 130 may include any type of device suitable fordetermining a location associated with at least one component of system100. In some embodiments, position sensor 130 may include a GPSreceiver. Such receivers can determine a user position and velocity byprocessing signals broadcasted by global positioning system satellites.Position information from position sensor 130 may be made available toapplications processor 180 and/or image processor 190.

User interface 170 may include any device suitable for providinginformation to or for receiving inputs from one or more users of system100. In some embodiments, user interface 170 may include user inputdevices, including, for example, a touchscreen, microphone, keyboard,pointer devices, track wheels, cameras, knobs, buttons, etc. With suchinput devices, a user may be able to provide information inputs orcommands to system 100 by typing instructions or information, providingvoice commands, selecting menu options on a screen using buttons,pointers, or eye-tracking capabilities, or through any other suitabletechniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devicesconfigured to provide and receive information to or from a user andprocess that information for use by, for example, applications processor180. In some embodiments, such processing devices may executeinstructions for recognizing and tracking eye movements, receiving andinterpreting voice commands, recognizing and interpreting touches and/orgestures made on a touchscreen, responding to keyboard entries or menuselections, etc. In some embodiments, user interface 170 may include adisplay, speaker, tactile device, and/or any other devices for providingoutput information to a user.

Map database 160 may include any type of database for storing map datauseful to system 100. In some embodiments, map database 160 may includedata relating to the position, in a reference coordinate system, ofvarious items, including roads, water features, geographic features,businesses, points of interest, restaurants, gas stations, etc. Mapdatabase 160 may store not only the locations of such items, but alsodescriptors relating to those items, including, for example, namesassociated with any of the stored features. In some embodiments, mapdatabase 160 may be physically located with other components of system100. Alternatively or additionally, map database 160 or a portionthereof may be located remotely with respect to other components ofsystem 100 (e.g., processing unit 110). In such embodiments, informationfrom map database 160 may be downloaded over a wired or wireless dataconnection to a network (e.g., over a cellular network and/or theInternet, etc.).

Image capture devices 122, 124, and 126 may each include any type ofdevice suitable for capturing at least one image from an environment.Moreover, any number of image capture devices may be used to acquireimages for input to the image processor. Some embodiments may includeonly a single image capture device, while other embodiments may includetwo, three, or even four or more image capture devices. Image capturedevices 122, 124, and 126 will be further described with reference toFIGS. 2B-2E, below.

System 100, or various components thereof, may be incorporated intovarious different platforms. In some embodiments, system 100 may beincluded on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200may be equipped with a processing unit 110 and any of the othercomponents of system 100, as described above relative to FIG. 1. Whilein some embodiments vehicle 200 may be equipped with only a single imagecapture device (e.g., camera), in other embodiments, such as thosediscussed in connection with FIGS. 2B-2E, multiple image capture devicesmay be used. For example, either of image capture devices 122 and 124 ofvehicle 200, as shown in FIG. 2A, may be part of an ADAS (AdvancedDriver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the imageacquisition unit 120 may be positioned at any suitable location. In someembodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122may be located in the vicinity of the rearview mirror. This position mayprovide a line of sight similar to that of the driver of vehicle 200,which may aid in determining what is and is not visible to the driver.Image capture device 122 may be positioned at any location near therearview mirror, but placing image capture device 122 on the driver sideof the mirror may further aid in obtaining images representative of thedriver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120 may also be used. For example, image capture device 124 may belocated on or in a bumper of vehicle 200. Such a location may beespecially suitable for image capture devices having a wide field ofview. The line of sight of bumper-located image capture devices can bedifferent from that of the driver and, therefore, the bumper imagecapture device and driver may not always see the same objects. The imagecapture devices (e.g., image capture devices 122, 124, and 126) may alsobe located in other locations. For example, the image capture devicesmay be located on or in one or both of the side mirrors of vehicle 200,on the roof of vehicle 200, on the hood of vehicle 200, on the trunk ofvehicle 200, on the sides of vehicle 200, mounted on, positioned behind,or positioned in front of any of the windows of vehicle 200, and mountedin or near light figures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include variousother components of system 100. For example, processing unit 110 may beincluded on vehicle 200 either integrated with or separate from anengine control unit (ECU) of the vehicle. Vehicle 200 may also beequipped with a position sensor 130, such as a GPS receiver and may alsoinclude a map database 160 and memory units 140 and 150.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle imaging system consistent with the disclosed embodiments. FIG.2B is a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated in FIG. 2B, the disclosed embodiments mayinclude a vehicle 200 including in its body a system 100 with a firstimage capture device 122 positioned in the vicinity of the rearviewmirror and/or near the driver of vehicle 200, a second image capturedevice 124 positioned on or in a bumper region (e.g., one of bumperregions 210) of vehicle 200, and a processing unit 110.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both bepositioned in the vicinity of the rearview mirror and/or near the driverof vehicle 200. Additionally, while two image capture devices 122 and124 are shown in FIGS. 2B and 2C, it should be understood that otherembodiments may include more than two image capture devices. Forexample, in the embodiments shown in FIGS. 2D and 2E, first, second, andthird image capture devices 122, 124, and 126, are included in thesystem 100 of vehicle 200.

As illustrated in FIG. 2D, image capture device 122 may be positioned inthe vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices 124 and 126 may be positioned on or in abumper region (e.g., one of bumper regions 210) of vehicle 200. And asshown in FIG. 2E, image capture devices 122, 124, and 126 may bepositioned in the vicinity of the rearview mirror and/or near the driverseat of vehicle 200. The disclosed embodiments are not limited to anyparticular number and configuration of the image capture devices, andthe image capture devices may be positioned in any appropriate locationwithin and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited tovehicles and could be applied in other contexts. It is also to beunderstood that disclosed embodiments are not limited to a particulartype of vehicle 200 and may be applicable to all types of vehiclesincluding automobiles, trucks, trailers, and other types of vehicles.

The first image capture device 122 may include any suitable type ofimage capture device. Image capture device 122 may include an opticalaxis. In one instance, the image capture device 122 may include anAptina M9V024 WVGA sensor with a global shutter. In other embodiments,image capture device 122 may provide a resolution of 1280×960 pixels andmay include a rolling shutter. Image capture device 122 may includevarious optical elements. In some embodiments one or more lenses may beincluded, for example, to provide a desired focal length and field ofview for the image capture device. In some embodiments, image capturedevice 122 may be associated with a 6 mm lens or a 12 mm lens. In someembodiments, image capture device 122 may be configured to captureimages having a desired field-of-view (FOV) 202, as illustrated in FIG.2D. For example, image capture device 122 may be configured to have aregular FOV, such as within a range of 40 degrees to 56 degrees,including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater.Alternatively, image capture device 122 may be configured to have anarrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or36 degree FOV. In addition, image capture device 122 may be configuredto have a wide FOV in the range of 100 to 180 degrees. In someembodiments, image capture device 122 may include a wide angle bumpercamera or one with up to a 180 degree FOV.

The first image capture device 122 may acquire a plurality of firstimages relative to a scene associated with the vehicle 200. Each of theplurality of first images may be acquired as a series of image scanlines, which may be captured using a rolling shutter. Each scan line mayinclude a plurality of pixels.

The first image capture device 122 may have a scan rate associated withacquisition of each of the first series of image scan lines. The scanrate may refer to a rate at which an image sensor can acquire image dataassociated with each pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable typeand number of image sensors, including CCD sensors or CMOS sensors, forexample. In one embodiment, a CMOS image sensor may be employed alongwith a rolling shutter, such that each pixel in a row is read one at atime, and scanning of the rows proceeds on a row-by-row basis until anentire image frame has been captured. In some embodiments, the rows maybe captured sequentially from top to bottom relative to the frame.

The use of a rolling shutter may result in pixels in different rowsbeing exposed and captured at different times, which may cause skew andother image artifacts in the captured image frame. On the other hand,when the image capture device 122 is configured to operate with a globalor synchronous shutter, all of the pixels may be exposed for the sameamount of time and during a common exposure period. As a result, theimage data in a frame collected from a system employing a global shutterrepresents a snapshot of the entire FOV (such as FOV 202) at aparticular time. In contrast, in a rolling shutter application, each rowin a frame is exposed and data is capture at different times. Thus,moving objects may appear distorted in an image capture device having arolling shutter. This phenomenon will be described in greater detailbelow.

The second image capture device 124 and the third image capturing device126 may be any type of image capture device. Like the first imagecapture device 122, each of image capture devices 124 and 126 mayinclude an optical axis. In one embodiment, each of image capturedevices 124 and 126 may include an Aptina M9V024 WVGA sensor with aglobal shutter. Alternatively, each of image capture devices 124 and 126may include a rolling shutter. Like image capture device 122, imagecapture devices 124 and 126 may be configured to include various lensesand optical elements. In some embodiments, lenses associated with imagecapture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206)that are the same as, or narrower than, a FOV (such as FOV 202)associated with image capture device 122. For example, image capturedevices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees,23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second andthird images relative to a scene associated with the vehicle 200. Eachof the plurality of second and third images may be acquired as a secondand third series of image scan lines, which may be captured using arolling shutter. Each scan line or row may have a plurality of pixels.Image capture devices 124 and 126 may have second and third scan ratesassociated with acquisition of each of image scan lines included in thesecond and third series.

Each image capture device 122, 124, and 126 may be positioned at anysuitable position and orientation relative to vehicle 200. The relativepositioning of the image capture devices 122, 124, and 126 may beselected to aid in fusing together the information acquired from theimage capture devices. For example, in some embodiments, a FOV (such asFOV 204) associated with image capture device 124 may overlap partiallyor fully with a FOV (such as FOV 202) associated with image capturedevice 122 and a FOV (such as FOV 206) associated with image capturedevice 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 atany suitable relative heights. In one instance, there may be a heightdifference between the image capture devices 122, 124, and 126, whichmay provide sufficient parallax information to enable stereo analysis.For example, as shown in FIG. 2A, the two image capture devices 122 and124 are at different heights. There may also be a lateral displacementdifference between image capture devices 122, 124, and 126, givingadditional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may bedenoted by d_(x), as shown in FIGS. 2C and 2D. In some embodiments, foreor aft displacement (e.g., range displacement) may exist between imagecapture devices 122, 124, and 126. For example, image capture device 122may be located 0.5 to 2 meters or more behind image capture device 124and/or image capture device 126. This type of displacement may enableone of the image capture devices to cover potential blind spots of theother image capture device(s).

Image capture devices 122 may have any suitable resolution capability(e.g., number of pixels associated with the image sensor), and theresolution of the image sensor(s) associated with the image capturedevice 122 may be higher, lower, or the same as the resolution of theimage sensor(s) associated with image capture devices 124 and 126. Insome embodiments, the image sensor(s) associated with image capturedevice 122 and/or image capture devices 124 and 126 may have aresolution of 640×480, 1024×768, 1280×960, or any other suitableresolution.

The frame rate (e.g., the rate at which an image capture device acquiresa set of pixel data of one image frame before moving on to capture pixeldata associated with the next image frame) may be controllable. Theframe rate associated with image capture device 122 may be higher,lower, or the same as the frame rate associated with image capturedevices 124 and 126. The frame rate associated with image capturedevices 122, 124, and 126 may depend on a variety of factors that mayaffect the timing of the frame rate. For example, one or more of imagecapture devices 122, 124, and 126 may include a selectable pixel delayperiod imposed before or after acquisition of image data associated withone or more pixels of an image sensor in image capture device 122, 124,and/or 126. Generally, image data corresponding to each pixel may beacquired according to a clock rate for the device (e.g., one pixel perclock cycle). Additionally, in embodiments including a rolling shutter,one or more of image capture devices 122, 124, and 126 may include aselectable horizontal blanking period imposed before or afteracquisition of image data associated with a row of pixels of an imagesensor in image capture device 122, 124, and/or 126. Further, one ormore of image capture devices 122, 124, and/or 126 may include aselectable vertical blanking period imposed before or after acquisitionof image data associated with an image frame of image capture device122, 124, and 126.

These timing controls may enable synchronization of frame ratesassociated with image capture devices 122, 124, and 126, even where theline scan rates of each are different. Additionally, as will bediscussed in greater detail below, these selectable timing controls,among other factors (e.g., image sensor resolution, maximum line scanrates, etc.) may enable synchronization of image capture from an areawhere the FOV of image capture device 122 overlaps with one or more FOVsof image capture devices 124 and 126, even where the field of view ofimage capture device 122 is different from the FOVs of image capturedevices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may dependon the resolution of the associated image sensors. For example, assumingsimilar line scan rates for both devices, if one device includes animage sensor having a resolution of 640×480 and another device includesan image sensor with a resolution of 1280×960, then more time will berequired to acquire a frame of image data from the sensor having thehigher resolution.

Another factor that may affect the timing of image data acquisition inimage capture devices 122, 124, and 126 is the maximum line scan rate.For example, acquisition of a row of image data from an image sensorincluded in image capture device 122, 124, and 126 will require someminimum amount of time. Assuming no pixel delay periods are added, thisminimum amount of time for acquisition of a row of image data will berelated to the maximum line scan rate for a particular device. Devicesthat offer higher maximum line scan rates have the potential to providehigher frame rates than devices with lower maximum line scan rates. Insome embodiments, one or more of image capture devices 124 and 126 mayhave a maximum line scan rate that is higher than a maximum line scanrate associated with image capture device 122. In some embodiments, themaximum line scan rate of image capture device 124 and/or 126 may be1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate ofimage capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may havethe same maximum line scan rate, but image capture device 122 may beoperated at a scan rate less than or equal to its maximum scan rate. Thesystem may be configured such that one or more of image capture devices124 and 126 operate at a line scan rate that is equal to the line scanrate of image capture device 122. In other instances, the system may beconfigured such that the line scan rate of image capture device 124and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times ormore than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may beasymmetric. That is, they may include cameras having different fields ofview (FOV) and focal lengths. The fields of view of image capturedevices 122, 124, and 126 may include any desired area relative to anenvironment of vehicle 200, for example. In some embodiments, one ormore of image capture devices 122, 124, and 126 may be configured toacquire image data from an environment in front of vehicle 200, behindvehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122,124, and/or 126 may be selectable (e.g., by inclusion of appropriatelenses etc.) such that each device acquires images of objects at adesired distance range relative to vehicle 200. For example, in someembodiments image capture devices 122, 124, and 126 may acquire imagesof close-up objects within a few meters from the vehicle. Image capturedevices 122, 124, and 126 may also be configured to acquire images ofobjects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100m, 150 m, or more). Further, the focal lengths of image capture devices122, 124, and 126 may be selected such that one image capture device(e.g., image capture device 122) can acquire images of objectsrelatively close to the vehicle (e.g., within 10 m or within 20 m) whilethe other image capture devices (e.g., image capture devices 124 and126) can acquire images of more distant objects (e.g., greater than 20m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capturedevices 122, 124, and 126 may have a wide angle. For example, it may beadvantageous to have a FOV of 140 degrees, especially for image capturedevices 122, 124, and 126 that may be used to capture images of the areain the vicinity of vehicle 200. For example, image capture device 122may be used to capture images of the area to the right or left ofvehicle 200 and, in such embodiments, it may be desirable for imagecapture device 122 to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices 122,124, and 126 may depend on the respective focal lengths. For example, asthe focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have anysuitable fields of view. In one particular example, image capture device122 may have a horizontal FOV of 46 degrees, image capture device 124may have a horizontal FOV of 23 degrees, and image capture device 126may have a horizontal FOV in between 23 and 46 degrees. In anotherinstance, image capture device 122 may have a horizontal FOV of 52degrees, image capture device 124 may have a horizontal FOV of 26degrees, and image capture device 126 may have a horizontal FOV inbetween 26 and 52 degrees. In some embodiments, a ratio of the FOV ofimage capture device 122 to the FOVs of image capture device 124 and/orimage capture device 126 may vary from 1.5 to 2.0. In other embodiments,this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capturedevice 122 overlaps, at least partially or fully, with a field of viewof image capture device 124 and/or image capture device 126. In someembodiments, system 100 may be configured such that the fields of viewof image capture devices 124 and 126, for example, fall within (e.g.,are narrower than) and share a common center with the field of view ofimage capture device 122. In other embodiments, the image capturedevices 122, 124, and 126 may capture adjacent FOVs or may have partialoverlap in their FOVs. In some embodiments, the fields of view of imagecapture devices 122, 124, and 126 may be aligned such that a center ofthe narrower FOV image capture devices 124 and/or 126 may be located ina lower half of the field of view of the wider FOV device 122.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems, consistent with the disclosed embodiments. As indicated in FIG.2F, vehicle 200 may include throttling system 220, braking system 230,and steering system 240. System 100 may provide inputs (e.g., controlsignals) to one or more of throttling system 220, braking system 230,and steering system 240 over one or more data links (e.g., any wiredand/or wireless link or links for transmitting data). For example, basedon analysis of images acquired by image capture devices 122, 124, and/or126, system 100 may provide control signals to one or more of throttlingsystem 220, braking system 230, and steering system 240 to navigatevehicle 200 (e.g., by causing an acceleration, a turn, a lane shift,etc.). Further, system 100 may receive inputs from one or more ofthrottling system 220, braking system 230, and steering system 24indicating operating conditions of vehicle 200 (e.g., speed, whethervehicle 200 is braking and/or turning, etc.). Further details areprovided in connection with FIGS. 4-7, below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170for interacting with a driver or a passenger of vehicle 200. Forexample, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver orpassenger of vehicle 200 may also use handles (e.g., located on or nearthe steering column of vehicle 200 including, for example, turn signalhandles), buttons (e.g., located on the steering wheel of vehicle 200),and the like, to interact with system 100. In some embodiments,microphone 350 may be positioned adjacent to a rearview mirror 310.Similarly, in some embodiments, image capture device 122 may be locatednear rearview mirror 310. In some embodiments, user interface 170 mayalso include one or more speakers 360 (e.g., speakers of a vehicle audiosystem). For example, system 100 may provide various notifications(e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370configured to be positioned behind a rearview mirror (e.g., rearviewmirror 310) and against a vehicle windshield, consistent with disclosedembodiments. As shown in FIG. 3B, camera mount 370 may include imagecapture devices 122, 124, and 126. Image capture devices 124 and 126 maybe positioned behind a glare shield 380, which may be flush against thevehicle windshield and include a composition of film and/oranti-reflective materials. For example, glare shield 380 may bepositioned such that it aligns against a vehicle windshield having amatching slope. In some embodiments, each of image capture devices 122,124, and 126 may be positioned behind glare shield 380, as depicted, forexample, in FIG. 3D. The disclosed embodiments are not limited to anyparticular configuration of image capture devices 122, 124, and 126,camera mount 370, and glare shield 380. FIG. 3C is an illustration ofcamera mount 370 shown in FIG. 3B from a front perspective.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the foregoing disclosed embodiments. For example, not all componentsare essential for the operation of system 100. Further, any componentmay be located in any appropriate part of system 100 and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments. Therefore, the foregoingconfigurations are examples and, regardless of the configurationsdiscussed above, system 100 can provide a wide range of functionality toanalyze the surroundings of vehicle 200 and navigate vehicle 200 inresponse to the analysis.

As discussed below in further detail and consistent with variousdisclosed embodiments, system 100 may provide a variety of featuresrelated to autonomous driving and/or driver assist technology. Forexample, system 100 may analyze image data, position data (e.g., GPSlocation information), map data, speed data, and/or data from sensorsincluded in vehicle 200. System 100 may collect the data for analysisfrom, for example, image acquisition unit 120, position sensor 130, andother sensors. Further, system 100 may analyze the collected data todetermine whether or not vehicle 200 should take a certain action, andthen automatically take the determined action without humanintervention. For example, when vehicle 200 navigates without humanintervention, system 100 may automatically control the braking,acceleration, and/or steering of vehicle 200 (e.g., by sending controlsignals to one or more of throttling system 220, braking system 230, andsteering system 240). Further, system 100 may analyze the collected dataand issue warnings and/or alerts to vehicle occupants based on theanalysis of the collected data. Additional details regarding the variousembodiments that are provided by system 100 are provided below.

Forward-Facing Multi-Imaging System

As discussed above, system 100 may provide drive assist functionalitythat uses a multi-camera system. The multi-camera system may use one ormore cameras facing in the forward direction of a vehicle. In otherembodiments, the multi-camera system may include one or more camerasfacing to the side of a vehicle or to the rear of the vehicle. In oneembodiment, for example, system 100 may use a two-camera imaging system,where a first camera and a second camera (e.g., image capture devices122 and 124) may be positioned at the front and/or the sides of avehicle (e.g., vehicle 200). The first camera may have a field of viewthat is greater than, less than, or partially overlapping with, thefield of view of the second camera. In addition, the first camera may beconnected to a first image processor to perform monocular image analysisof images provided by the first camera, and the second camera may beconnected to a second image processor to perform monocular imageanalysis of images provided by the second camera. The outputs (e.g.,processed information) of the first and second image processors may becombined. In some embodiments, the second image processor may receiveimages from both the first camera and second camera to perform stereoanalysis. In another embodiment, system 100 may use a three-cameraimaging system where each of the cameras has a different field of view.Such a system may, therefore, make decisions based on informationderived from objects located at varying distances both forward and tothe sides of the vehicle. References to monocular image analysis mayrefer to instances where image analysis is performed based on imagescaptured from a single point of view (e.g., from a single camera).Stereo image analysis may refer to instances where image analysis isperformed based on two or more images captured with one or morevariations of an image capture parameter. For example, captured imagessuitable for performing stereo image analysis may include imagescaptured: from two or more different positions, from different fields ofview, using different focal lengths, along with parallax information,etc.

For example, in one embodiment, system 100 may implement a three cameraconfiguration using image capture devices 122-126. In such aconfiguration, image capture device 122 may provide a narrow field ofview (e.g., 34 degrees, or other values selected from a range of about20 to 45 degrees, etc.), image capture device 124 may provide a widefield of view (e.g., 150 degrees or other values selected from a rangeof about 100 to about 180 degrees), and image capture device 126 mayprovide an intermediate field of view (e.g., 46 degrees or other valuesselected from a range of about 35 to about 60 degrees). In someembodiments, image capture device 126 may act as a main or primarycamera. Image capture devices 122-126 may be positioned behind rearviewmirror 310 and positioned substantially side-by-side (e.g., 6 cm apart).Further, in some embodiments, as discussed above, one or more of imagecapture devices 122-126 may be mounted behind glare shield 380 that isflush with the windshield of vehicle 200. Such shielding may act tominimize the impact of any reflections from inside the car on imagecapture devices 122-126.

In another embodiment, as discussed above in connection with FIGS. 3Band 3C, the wide field of view camera (e.g., image capture device 124 inthe above example) may be mounted lower than the narrow and main fieldof view cameras (e.g., image devices 122 and 126 in the above example).This configuration may provide a free line of sight from the wide fieldof view camera. To reduce reflections, the cameras may be mounted closeto the windshield of vehicle 200, and may include polarizers on thecameras to damp reflected light.

A three camera system may provide certain performance characteristics.For example, some embodiments may include an ability to validate thedetection of objects by one camera based on detection results fromanother camera. In the three camera configuration discussed above,processing unit 110 may include, for example, three processing devices(e.g., three EyeQ series of processor chips, as discussed above), witheach processing device dedicated to processing images captured by one ormore of image capture devices 122-126.

In a three camera system, a first processing device may receive imagesfrom both the main camera and the narrow field of view camera, andperform vision processing of the narrow FOV camera to, for example,detect other vehicles, pedestrians, lane marks, traffic signs, trafficlights, and other road objects. Further, the first processing device maycalculate a disparity of pixels between the images from the main cameraand the narrow camera and create a 3D reconstruction of the environmentof vehicle 200. The first processing device may then combine the 3Dreconstruction with 3D map data or with 3D information calculated basedon information from another camera.

The second processing device may receive images from main camera andperform vision processing to detect other vehicles, pedestrians, lanemarks, traffic signs, traffic lights, and other road objects.Additionally, the second processing device may calculate a cameradisplacement and, based on the displacement, calculate a disparity ofpixels between successive images and create a 3D reconstruction of thescene (e.g., a structure from motion). The second processing device maysend the structure from motion based 3D reconstruction to the firstprocessing device to be combined with the stereo 3D images.

The third processing device may receive images from the wide FOV cameraand process the images to detect vehicles, pedestrians, lane marks,traffic signs, traffic lights, and other road objects. The thirdprocessing device may further execute additional processing instructionsto analyze images to identify objects moving in the image, such asvehicles changing lanes, pedestrians, etc.

In some embodiments, having streams of image-based information capturedand processed independently may provide an opportunity for providingredundancy in the system. Such redundancy may include, for example,using a first image capture device and the images processed from thatdevice to validate and/or supplement information obtained by capturingand processing image information from at least a second image capturedevice.

In some embodiments, system 100 may use two image capture devices (e.g.,image capture devices 122 and 124) in providing navigation assistancefor vehicle 200 and use a third image capture device (e.g., imagecapture device 126) to provide redundancy and validate the analysis ofdata received from the other two image capture devices. For example, insuch a configuration, image capture devices 122 and 124 may provideimages for stereo analysis by system 100 for navigating vehicle 200,while image capture device 126 may provide images for monocular analysisby system 100 to provide redundancy and validation of informationobtained based on images captured from image capture device 122 and/orimage capture device 124. That is, image capture device 126 (and acorresponding processing device) may be considered to provide aredundant sub-system for providing a check on the analysis derived fromimage capture devices 122 and 124 (e.g., to provide an automaticemergency braking (AEB) system).

One of skill in the art will recognize that the above cameraconfigurations, camera placements, number of cameras, camera locations,etc., are examples only. These components and others described relativeto the overall system may be assembled and used in a variety ofdifferent configurations without departing from the scope of thedisclosed embodiments. Further details regarding usage of a multi-camerasystem to provide driver assist and/or autonomous vehicle functionalityfollow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4, memory 140 may store a monocular image analysismodule 402, a stereo image analysis module 404, a velocity andacceleration module 406, and a navigational response module 408. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, application processor 180 and/or image processor190 may execute the instructions stored in any of modules 402-408included in memory 140. One of skill in the art will understand thatreferences in the following discussions to processing unit 110 may referto application processor 180 and image processor 190 individually orcollectively. Accordingly, steps of any of the following processes maybe performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar) to perform the monocular image analysis. As described inconnection with FIGS. 5A-5D below, monocular image analysis module 402may include instructions for detecting a set of features within the setof images, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, hazardous objects, and any otherfeature associated with an environment of a vehicle. Based on theanalysis, system 100 (e.g., via processing unit 110) may cause one ormore navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with navigational response module 408.

In one embodiment, stereo image analysis module 404 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs stereo image analysis of first and secondsets of images acquired by a combination of image capture devicesselected from any of image capture devices 122, 124, and 126. In someembodiments, processing unit 110 may combine information from the firstand second sets of images with additional sensory information (e.g.,information from radar) to perform the stereo image analysis. Forexample, stereo image analysis module 404 may include instructions forperforming stereo image analysis based on a first set of images acquiredby image capture device 124 and a second set of images acquired by imagecapture device 126. As described in connection with FIG. 6 below, stereoimage analysis module 404 may include instructions for detecting a setof features within the first and second sets of images, such as lanemarkings, vehicles, pedestrians, road signs, highway exit ramps, trafficlights, hazardous objects, and the like. Based on the analysis,processing unit 110 may cause one or more navigational responses invehicle 200, such as a turn, a lane shift, a change in acceleration, andthe like, as discussed below in connection with navigational responsemodule 408.

In one embodiment, velocity and acceleration module 406 may storesoftware configured to analyze data received from one or more computingand electromechanical devices in vehicle 200 that are configured tocause a change in velocity and/or acceleration of vehicle 200. Forexample, processing unit 110 may execute instructions associated withvelocity and acceleration module 406 to calculate a target speed forvehicle 200 based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include, for example, a target position, velocity, and/oracceleration, the position and/or speed of vehicle 200 relative to anearby vehicle, pedestrian, or road object, position information forvehicle 200 relative to lane markings of the road, and the like. Inaddition, processing unit 110 may calculate a target speed for vehicle200 based on sensory input (e.g., information from radar) and input fromother systems of vehicle 200, such as throttling system 220, brakingsystem 230, and/or steering system 240 of vehicle 200. Based on thecalculated target speed, processing unit 110 may transmit electronicsignals to throttling system 220, braking system 230, and/or steeringsystem 240 of vehicle 200 to trigger a change in velocity and/oracceleration by, for example, physically depressing the brake or easingup off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include position and speed information associated with nearbyvehicles, pedestrians, and road objects, target position information forvehicle 200, and the like. Additionally, in some embodiments, thenavigational response may be based (partially or fully) on map data, apredetermined position of vehicle 200, and/or a relative velocity or arelative acceleration between vehicle 200 and one or more objectsdetected from execution of monocular image analysis module 402 and/orstereo image analysis module 404. Navigational response module 408 mayalso determine a desired navigational response based on sensory input(e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system 220, braking system 230, and steeringsystem 240 of vehicle 200. Based on the desired navigational response,processing unit 110 may transmit electronic signals to throttling system220, braking system 230, and steering system 240 of vehicle 200 totrigger a desired navigational response by, for example, turning thesteering wheel of vehicle 200 to achieve a rotation of a predeterminedangle. In some embodiments, processing unit 110 may use the output ofnavigational response module 408 (e.g., the desired navigationalresponse) as an input to execution of velocity and acceleration module406 for calculating a change in speed of vehicle 200.

FIG. 5A is a flowchart showing an exemplary process 500A for causing oneor more navigational responses based on monocular image analysis,consistent with disclosed embodiments. At step 510, processing unit 110may receive a plurality of images via data interface 128 betweenprocessing unit 110 and image acquisition unit 120. For instance, acamera included in image acquisition unit 120 (such as image capturedevice 122 having field of view 202) may capture a plurality of imagesof an area forward of vehicle 200 (or to the sides or rear of a vehicle,for example) and transmit them over a data connection (e.g., digital,wired, USB, wireless, Bluetooth, etc.) to processing unit 110.Processing unit 110 may execute monocular image analysis module 402 toanalyze the plurality of images at step 520, as described in furtherdetail in connection with FIGS. 5B-5D below. By performing the analysis,processing unit 110 may detect a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402to detect various road hazards at step 520, such as, for example, partsof a truck tire, fallen road signs, loose cargo, small animals, and thelike. Road hazards may vary in structure, shape, size, and color, whichmay make detection of such hazards more challenging. In someembodiments, processing unit 110 may execute monocular image analysismodule 402 to perform multi-frame analysis on the plurality of images todetect road hazards. For example, processing unit 110 may estimatecamera motion between consecutive image frames and calculate thedisparities in pixels between the frames to construct a 3D-map of theroad. Processing unit 110 may then use the 3D-map to detect the roadsurface, as well as hazards existing above the road surface.

At step 530, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 520 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration,and the like. In some embodiments, processing unit 110 may use dataderived from execution of velocity and acceleration module 406 to causethe one or more navigational responses. Additionally, multiplenavigational responses may occur simultaneously, in sequence, or anycombination thereof. For instance, processing unit 110 may cause vehicle200 to shift one lane over and then accelerate by, for example,sequentially transmitting control signals to steering system 240 andthrottling system 220 of vehicle 200. Alternatively, processing unit 110may cause vehicle 200 to brake while at the same time shifting lanes by,for example, simultaneously transmitting control signals to brakingsystem 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detectingone or more vehicles and/or pedestrians in a set of images, consistentwith disclosed embodiments. Processing unit 110 may execute monocularimage analysis module 402 to implement process 500B. At step 540,processing unit 110 may determine a set of candidate objectsrepresenting possible vehicles and/or pedestrians. For example,processing unit 110 may scan one or more images, compare the images toone or more predetermined patterns, and identify within each imagepossible locations that may contain objects of interest (e.g., vehicles,pedestrians, or portions thereof). The predetermined patterns may bedesigned in such a way to achieve a high rate of “false hits” and a lowrate of “misses.” For example, processing unit 110 may use a lowthreshold of similarity to predetermined patterns for identifyingcandidate objects as possible vehicles or pedestrians. Doing so mayallow processing unit 110 to reduce the probability of missing (e.g.,not identifying) a candidate object representing a vehicle orpedestrian.

At step 542, processing unit 110 may filter the set of candidate objectsto exclude certain candidates (e.g., irrelevant or less relevantobjects) based on classification criteria. Such criteria may be derivedfrom various properties associated with object types stored in adatabase (e.g., a database stored in memory 140). Properties may includeobject shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit 110 may use one or more setsof criteria to reject false candidates from the set of candidateobjects.

At step 544, processing unit 110 may analyze multiple frames of imagesto determine whether objects in the set of candidate objects representvehicles and/or pedestrians. For example, processing unit 110 may tracka detected candidate object across consecutive frames and accumulateframe-by-frame data associated with the detected object (e.g., size,position relative to vehicle 200, etc.). Additionally, processing unit110 may estimate parameters for the detected object and compare theobject's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements forthe detected objects. Such measurements may include, for example,position, velocity, and acceleration values (relative to vehicle 200)associated with the detected objects. In some embodiments, processingunit 110 may construct the measurements based on estimation techniquesusing a series of time-based observations such as Kalman filters orlinear quadratic estimation (LQE), and/or based on available modelingdata for different object types (e.g., cars, trucks, pedestrians,bicycles, road signs, etc.). The Kalman filters may be based on ameasurement of an object's scale, where the scale measurement isproportional to a time to collision (e.g., the amount of time forvehicle 200 to reach the object). Thus, by performing steps 540-546,processing unit 110 may identify vehicles and pedestrians appearingwithin the set of captured images and derive information (e.g.,position, speed, size) associated with the vehicles and pedestrians.Based on the identification and the derived information, processing unit110 may cause one or more navigational responses in vehicle 200, asdescribed in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis ofone or more images to reduce the probabilities of detecting a “falsehit” and missing a candidate object that represents a vehicle orpedestrian. The optical flow analysis may refer to, for example,analyzing motion patterns relative to vehicle 200 in the one or moreimages associated with other vehicles and pedestrians, and that aredistinct from road surface motion. Processing unit 110 may calculate themotion of candidate objects by observing the different positions of theobjects across multiple image frames, which are captured at differenttimes. Processing unit 110 may use the position and time values asinputs into mathematical models for calculating the motion of thecandidate objects. Thus, optical flow analysis may provide anothermethod of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit 110 may perform optical flow analysis incombination with steps 540-546 to provide redundancy for detectingvehicles and pedestrians and increase the reliability of system 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detectingroad marks and/or lane geometry information in a set of images,consistent with disclosed embodiments. Processing unit 110 may executemonocular image analysis module 402 to implement process 500C. At step550, processing unit 110 may detect a set of objects by scanning one ormore images. To detect segments of lane markings, lane geometryinformation, and other pertinent road marks, processing unit 110 mayfilter the set of objects to exclude those determined to be irrelevant(e.g., minor potholes, small rocks, etc.). At step 552, processing unit110 may group together the segments detected in step 550 belonging tothe same road mark or lane mark. Based on the grouping, processing unit110 may develop a model to represent the detected segments, such as amathematical model.

At step 554, processing unit 110 may construct a set of measurementsassociated with the detected segments. In some embodiments, processingunit 110 may create a projection of the detected segments from the imageplane onto the real-world plane. The projection may be characterizedusing a 3rd-degree polynomial having coefficients corresponding tophysical properties such as the position, slope, curvature, andcurvature derivative of the detected road. In generating the projection,processing unit 110 may take into account changes in the road surface,as well as pitch and roll rates associated with vehicle 200. Inaddition, processing unit 110 may model the road elevation by analyzingposition and motion cues present on the road surface. Further,processing unit 110 may estimate the pitch and roll rates associatedwith vehicle 200 by tracking a set of feature points in the one or moreimages.

At step 556, processing unit 110 may perform multi-frame analysis by,for example, tracking the detected segments across consecutive imageframes and accumulating frame-by-frame data associated with detectedsegments. As processing unit 110 performs multi-frame analysis, the setof measurements constructed at step 554 may become more reliable andassociated with an increasingly higher confidence level. Thus, byperforming steps 550-556, processing unit 110 may identify road marksappearing within the set of captured images and derive lane geometryinformation. Based on the identification and the derived information,processing unit 110 may cause one or more navigational responses invehicle 200, as described in connection with FIG. 5A, above.

At step 558, processing unit 110 may consider additional sources ofinformation to further develop a safety model for vehicle 200 in thecontext of its surroundings. Processing unit 110 may use the safetymodel to define a context in which system 100 may execute autonomouscontrol of vehicle 200 in a safe manner. To develop the safety model, insome embodiments, processing unit 110 may consider the position andmotion of other vehicles, the detected road edges and barriers, and/orgeneral road shape descriptions extracted from map data (such as datafrom map database 160). By considering additional sources ofinformation, processing unit 110 may provide redundancy for detectingroad marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detectingtraffic lights in a set of images, consistent with disclosedembodiments. Processing unit 110 may execute monocular image analysismodule 402 to implement process 500D. At step 560, processing unit 110may scan the set of images and identify objects appearing at locationsin the images likely to contain traffic lights. For example, processingunit 110 may filter the identified objects to construct a set ofcandidate objects, excluding those objects unlikely to correspond totraffic lights. The filtering may be done based on various propertiesassociated with traffic lights, such as shape, dimensions, texture,position (e.g., relative to vehicle 200), and the like. Such propertiesmay be based on multiple examples of traffic lights and traffic controlsignals and stored in a database. In some embodiments, processing unit110 may perform multi-frame analysis on the set of candidate objectsreflecting possible traffic lights. For example, processing unit 110 maytrack the candidate objects across consecutive image frames, estimatethe real-world position of the candidate objects, and filter out thoseobjects that are moving (which are unlikely to be traffic lights). Insome embodiments, processing unit 110 may perform color analysis on thecandidate objects and identify the relative position of the detectedcolors appearing inside possible traffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction.The analysis may be based on any combination of: (i) the number of lanesdetected on either side of vehicle 200, (ii) markings (such as arrowmarks) detected on the road, and (iii) descriptions of the junctionextracted from map data (such as data from map database 160). Processingunit 110 may conduct the analysis using information derived fromexecution of monocular analysis module 402. In addition, Processing unit110 may determine a correspondence between the traffic lights detectedat step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110may update the confidence level associated with the analyzed junctiongeometry and the detected traffic lights. For instance, the number oftraffic lights estimated to appear at the junction as compared with thenumber actually appearing at the junction may impact the confidencelevel. Thus, based on the confidence level, processing unit 110 maydelegate control to the driver of vehicle 200 in order to improve safetyconditions. By performing steps 560-564, processing unit 110 mayidentify traffic lights appearing within the set of captured images andanalyze junction geometry information. Based on the identification andthe analysis, processing unit 110 may cause one or more navigationalresponses in vehicle 200, as described in connection with FIG. 5A,above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing oneor more navigational responses in vehicle 200 based on a vehicle path,consistent with the disclosed embodiments. At step 570, processing unit110 may construct an initial vehicle path associated with vehicle 200.The vehicle path may be represented using a set of points expressed incoordinates (x, z), and the distance d_(i) between two points in the setof points may fall in the range of 1 to 5 meters. In one embodiment,processing unit 110 may construct the initial vehicle path using twopolynomials, such as left and right road polynomials. Processing unit110 may calculate the geometric midpoint between the two polynomials andoffset each point included in the resultant vehicle path by apredetermined offset (e.g., a smart lane offset), if any (an offset ofzero may correspond to travel in the middle of a lane). The offset maybe in a direction perpendicular to a segment between any two points inthe vehicle path. In another embodiment, processing unit 110 may use onepolynomial and an estimated lane width to offset each point of thevehicle path by half the estimated lane width plus a predeterminedoffset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructedat step 570. Processing unit 110 may reconstruct the vehicle pathconstructed at step 570 using a higher resolution, such that thedistance d_(k) between two points in the set of points representing thevehicle path is less than the distance d_(i) described above. Forexample, the distance d_(k) may fall in the range of 0.1 to 0.3 meters.Processing unit 110 may reconstruct the vehicle path using a parabolicspline algorithm, which may yield a cumulative distance vector Scorresponding to the total length of the vehicle path (i.e., based onthe set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point(expressed in coordinates as (x_(l), z_(l))) based on the updatedvehicle path constructed at step 572. Processing unit 110 may extractthe look-ahead point from the cumulative distance vector S, and thelook-ahead point may be associated with a look-ahead distance andlook-ahead time. The look-ahead distance, which may have a lower boundranging from 10 to 20 meters, may be calculated as the product of thespeed of vehicle 200 and the look-ahead time. For example, as the speedof vehicle 200 decreases, the look-ahead distance may also decrease(e.g., until it reaches the lower bound). The look-ahead time, which mayrange from 0.5 to 1.5 seconds, may be inversely proportional to the gainof one or more control loops associated with causing a navigationalresponse in vehicle 200, such as the heading error tracking controlloop. For example, the gain of the heading error tracking control loopmay depend on the bandwidth of a yaw rate loop, a steering actuatorloop, car lateral dynamics, and the like. Thus, the higher the gain ofthe heading error tracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yawrate command based on the look-ahead point determined at step 574.Processing unit 110 may determine the heading error by calculating thearctangent of the look-ahead point, e.g., arctan (x_(l)/z_(l)).Processing unit 110 may determine the yaw rate command as the product ofthe heading error and a high-level control gain. The high-level controlgain may be equal to: (2/look-ahead time), if the look-ahead distance isnot at the lower bound. Otherwise, the high-level control gain may beequal to: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determiningwhether a leading vehicle is changing lanes, consistent with thedisclosed embodiments. At step 580, processing unit 110 may determinenavigation information associated with a leading vehicle (e.g., avehicle traveling ahead of vehicle 200). For example, processing unit110 may determine the position, velocity (e.g., direction and speed),and/or acceleration of the leading vehicle, using the techniquesdescribed in connection with FIGS. 5A and 5B, above. Processing unit 110may also determine one or more road polynomials, a look-ahead point(associated with vehicle 200), and/or a snail trail (e.g., a set ofpoints describing a path taken by the leading vehicle), using thetechniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation informationdetermined at step 580. In one embodiment, processing unit 110 maycalculate the distance between a snail trail and a road polynomial(e.g., along the trail). If the variance of this distance along thetrail exceeds a predetermined threshold (for example, 0.1 to 0.2 meterson a straight road, 0.3 to 0.4 meters on a moderately curvy road, and0.5 to 0.6 meters on a road with sharp curves), processing unit 110 maydetermine that the leading vehicle is likely changing lanes. In the casewhere multiple vehicles are detected traveling ahead of vehicle 200,processing unit 110 may compare the snail trails associated with eachvehicle. Based on the comparison, processing unit 110 may determine thata vehicle whose snail trail does not match with the snail trails of theother vehicles is likely changing lanes. Processing unit 110 mayadditionally compare the curvature of the snail trail (associated withthe leading vehicle) with the expected curvature of the road segment inwhich the leading vehicle is traveling. The expected curvature may beextracted from map data (e.g., data from map database 160), from roadpolynomials, from other vehicles' snail trails, from prior knowledgeabout the road, and the like. If the difference in curvature of thesnail trail and the expected curvature of the road segment exceeds apredetermined threshold, processing unit 110 may determine that theleading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leadingvehicle's instantaneous position with the look-ahead point (associatedwith vehicle 200) over a specific period of time (e.g., 0.5 to 1.5seconds). If the distance between the leading vehicle's instantaneousposition and the look-ahead point varies during the specific period oftime, and the cumulative sum of variation exceeds a predeterminedthreshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8meters on a moderately curvy road, and 1.3 to 1.7 meters on a road withsharp curves), processing unit 110 may determine that the leadingvehicle is likely changing lanes. In another embodiment, processing unit110 may analyze the geometry of the snail trail by comparing the lateraldistance traveled along the trail with the expected curvature of thesnail trail. The expected radius of curvature may be determinedaccording to the calculation: (δ_(z) ²+δ_(x) ²)/2/(δ_(x)), where δ_(x)represents the lateral distance traveled and δ_(z) represents thelongitudinal distance traveled. If the difference between the lateraldistance traveled and the expected curvature exceeds a predeterminedthreshold (e.g., 500 to 700 meters), processing unit 110 may determinethat the leading vehicle is likely changing lanes. In anotherembodiment, processing unit 110 may analyze the position of the leadingvehicle. If the position of the leading vehicle obscures a roadpolynomial (e.g., the leading vehicle is overlaid on top of the roadpolynomial), then processing unit 110 may determine that the leadingvehicle is likely changing lanes. In the case where the position of theleading vehicle is such that, another vehicle is detected ahead of theleading vehicle and the snail trails of the two vehicles are notparallel, processing unit 110 may determine that the (closer) leadingvehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leadingvehicle 200 is changing lanes based on the analysis performed at step582. For example, processing unit 110 may make the determination basedon a weighted average of the individual analyses performed at step 582.Under such a scheme, for example, a decision by processing unit 110 thatthe leading vehicle is likely changing lanes based on a particular typeof analysis may be assigned a value of “1” (and “0” to represent adetermination that the leading vehicle is not likely changing lanes).Different analyses performed at step 582 may be assigned differentweights, and the disclosed embodiments are not limited to any particularcombination of analyses and weights.

FIG. 6 is a flowchart showing an exemplary process 600 for causing oneor more navigational responses based on stereo image analysis,consistent with disclosed embodiments. At step 610, processing unit 110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit 120 (suchas image capture devices 122 and 124 having fields of view 202 and 204)may capture a first and second plurality of images of an area forward ofvehicle 200 and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110. In some embodiments,processing unit 110 may receive the first and second plurality of imagesvia two or more data interfaces. The disclosed embodiments are notlimited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysismodule 404 to perform stereo image analysis of the first and secondplurality of images to create a 3D map of the road in front of thevehicle and detect features within the images, such as lane markings,vehicles, pedestrians, road signs, highway exit ramps, traffic lights,road hazards, and the like. Stereo image analysis may be performed in amanner similar to the steps described in connection with FIGS. 5A-5D,above. For example, processing unit 110 may execute stereo imageanalysis module 404 to detect candidate objects (e.g., vehicles,pedestrians, road marks, traffic lights, road hazards, etc.) within thefirst and second plurality of images, filter out a subset of thecandidate objects based on various criteria, and perform multi-frameanalysis, construct measurements, and determine a confidence level forthe remaining candidate objects. In performing the steps above,processing unit 110 may consider information from both the first andsecond plurality of images, rather than information from one set ofimages alone. For example, processing unit 110 may analyze thedifferences in pixel-level data (or other data subsets from among thetwo streams of captured images) for a candidate object appearing in boththe first and second plurality of images. As another example, processingunit 110 may estimate a position and/or velocity of a candidate object(e.g., relative to vehicle 200) by observing that the object appears inone of the plurality of images but not the other or relative to otherdifferences that may exist relative to objects appearing if the twoimage streams. For example, position, velocity, and/or accelerationrelative to vehicle 200 may be determined based on trajectories,positions, movement characteristics, etc. of features associated with anobject appearing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 620 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration, achange in velocity, braking, and the like. In some embodiments,processing unit 110 may use data derived from execution of velocity andacceleration module 406 to cause the one or more navigational responses.Additionally, multiple navigational responses may occur simultaneously,in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing oneor more navigational responses based on an analysis of three sets ofimages, consistent with disclosed embodiments. At step 710, processingunit 110 may receive a first, second, and third plurality of images viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture a first, second, and thirdplurality of images of an area forward and/or to the side of vehicle 200and transmit them over a digital connection (e.g., USB, wireless,Bluetooth, etc.) to processing unit 110. In some embodiments, processingunit 110 may receive the first, second, and third plurality of imagesvia three or more data interfaces. For example, each of image capturedevices 122, 124, 126 may have an associated data interface forcommunicating data to processing unit 110. The disclosed embodiments arenot limited to any particular data interface configurations orprotocols.

At step 720, processing unit 110 may analyze the first, second, andthird plurality of images to detect features within the images, such aslane markings, vehicles, pedestrians, road signs, highway exit ramps,traffic lights, road hazards, and the like. The analysis may beperformed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6, above. For instance, processing unit 110 may performmonocular image analysis (e.g., via execution of monocular imageanalysis module 402 and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality ofimages. Alternatively, processing unit 110 may perform stereo imageanalysis (e.g., via execution of stereo image analysis module 404 andbased on the steps described in connection with FIG. 6, above) on thefirst and second plurality of images, the second and third plurality ofimages, and/or the first and third plurality of images. The processedinformation corresponding to the analysis of the first, second, and/orthird plurality of images may be combined. In some embodiments,processing unit 110 may perform a combination of monocular and stereoimage analyses. For example, processing unit 110 may perform monocularimage analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g.,via execution of stereo image analysis module 404) on the second andthird plurality of images. The configuration of image capture devices122, 124, and 126—including their respective locations and fields ofview 202, 204, and 206—may influence the types of analyses conducted onthe first, second, and third plurality of images. The disclosedembodiments are not limited to a particular configuration of imagecapture devices 122, 124, and 126, or the types of analyses conducted onthe first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system100 based on the images acquired and analyzed at steps 710 and 720. Suchtesting may provide an indicator of the overall performance of system100 for certain configurations of image capture devices 122, 124, and126. For example, processing unit 110 may determine the proportion of“false hits” (e.g., cases where system 100 incorrectly determined thepresence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigationalresponses in vehicle 200 based on information derived from two of thefirst, second, and third plurality of images. Selection of two of thefirst, second, and third plurality of images may depend on variousfactors, such as, for example, the number, types, and sizes of objectsdetected in each of the plurality of images. Processing unit 110 mayalso make the selection based on image quality and resolution, theeffective field of view reflected in the images, the number of capturedframes, the extent to which one or more objects of interest actuallyappear in the frames (e.g., the percentage of frames in which an objectappears, the proportion of the object that appears in each such frame,etc.), and the like.

In some embodiments, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images bydetermining the extent to which information derived from one imagesource is consistent with information derived from other image sources.For example, processing unit 110 may combine the processed informationderived from each of image capture devices 122, 124, and 126 (whether bymonocular analysis, stereo analysis, or any combination of the two) anddetermine visual indicators (e.g., lane markings, a detected vehicle andits location and/or path, a detected traffic light, etc.) that areconsistent across the images captured from each of image capture devices122, 124, and 126. Processing unit 110 may also exclude information thatis inconsistent across the captured images (e.g., a vehicle changinglanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images based onthe determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, achange in acceleration, and the like. Processing unit 110 may cause theone or more navigational responses based on the analysis performed atstep 720 and the techniques as described above in connection with FIG.4. Processing unit 110 may also use data derived from execution ofvelocity and acceleration module 406 to cause the one or morenavigational responses. In some embodiments, processing unit 110 maycause the one or more navigational responses based on a relativeposition, relative velocity, and/or relative acceleration betweenvehicle 200 and an object detected within any of the first, second, andthird plurality of images. Multiple navigational responses may occursimultaneously, in sequence, or any combination thereof.

Curb Detection and Pedestrian Hazard Assessment

As described herein, system 100 may include technical features andcapabilities that allow for the detection of certain elements thatassist system 100 with staying on a road and avoiding collisions. In oneexample, system 100 is configured to detect road boundaries that definea roadway and/or pedestrians that are in the vicinity of the roadway.Knowledge of a physical road boundary allows system 100 to either alerta driver or control the vehicle in such a way as to avoid driving off ofthe road and/or colliding with an object or pedestrian. For example,identifying a road boundary helps to provide physical protection topedestrians on a sidewalk adjacent to the road by demarcating the regionin which pedestrians are likely to be present.

In an exemplary embodiment, system 100 may be configured to detect acurb as a road boundary. A curb in most situations is a physicalboundary that separates the road surface from an off-road surface (e.g.,sidewalk, path, median, building, etc.). Keeping a vehicle from crossinga curb helps to keep the vehicle on the road and pedestrians on thesidewalk safe. However, pedestrians situated on the road surface are ata greater risk of being hit by moving vehicles. Therefore, an ability toclassify pedestrians as either on-curb or off-curb, in addition to anestimation of the vehicle and pedestrian trajectory provides system 100with an ability to assess a hazard posed by the vehicle to pedestrians.Disclosed embodiments include curb detection and pedestrian hazardassessment features to provide these and other functionalities to system100.

To implement the curb detection and pedestrian hazard assessmentfunctionality, processing unit 110 may receive images captured by atleast one of image capture devices 122, 124, 126 and perform one or moreimage analysis processes. For example, processing unit 110 may perform acurb identification process to identify a curb in the captured imagesand a pedestrian identification process to determine a location ofpedestrians relative to a road surface.

In some embodiments, memory 140 and/or 150 may store instructionsprogrammed such that, upon execution by a processing device, curbdetection and/or pedestrian hazard assessment functions are provided. Asshown in FIG. 8, memory 140 and/or 150 may store a curb identificationmodule 810, a curb registration module 820, a pedestrian identificationmodule 830, and a database 840. Curb identification module 810 may storeinstructions for identifying elements of an image that correspond to acurb. Curb registration module 820 may store instructions for modeling acurb and/or determining the properties of each identified curb.Pedestrian identification module 830 may store instructions fordetecting and classifying a pedestrian in one or more captured images.Database 840 may be configured to store data associated with curbdetection and/or pedestrian hazard assessment functions. Further, curbidentification module 810, curb registration module 820, and pedestrianidentification module 830 may store instructions executable by one ormore processors (e.g., processing unit 110), alone or in variouscombinations with each other. For example, curb identification module810, curb registration module 820, and pedestrian identification module830 may be configured to interact with each other and/or other modulesof system 100 to perform functions consistent with disclosedembodiments.

Database 840 may include one or more memory devices that storeinformation and are accessed and/or managed through a computing device,such as processing unit 110. In some embodiments, database 840 may belocated in memory 140 or 150, as shown in FIG. 8. In other embodiments,database 840 may be located remotely from memory 140 or 150, and beaccessible to other components of system 100 (e.g., processing unit 120)via one or more wireless connections (e.g., a wireless network). Whileone database 840 is shown, it should be understood that several separateand/or interconnected databases may make up database 840. Database 840may include computing components (e.g., database management system,database server, etc.) configured to receive and process requests fordata stored in memory devices associated with database 840 and toprovide data from database 840 (e.g., to processing unit 110).

In some embodiments, database 840 may be configured to store dataassociated with providing curb detection and/or pedestrian hazardassessment functions. For example, database 840 may store data, such asimages, parts of images, maps, algorithms, sets of values, or the like,which may allow processing unit 110 to identify information detected inan image. For instance, database 840 may store information that allowssystem 100 to identify a curb and/or pedestrians, such as stored imagesof curbs and/or images of pedestrians.

FIG. 9 depicts an exemplary image 900 captured by one of image capturedevices 122, 124, 126. Image 900 includes an environment that maygenerally represent a setting that is seen as a vehicle travels on aroad. For example, image 900 includes a road 910 defined betweenopposing curbs 920, and a plurality of pedestrians 930. In an exemplaryembodiment, system 100 may be configured to identify and/ordifferentiate between the various features of image 900. Knowledge ofthese features allows system 100 to make decisions that help to promotesafe operation of the vehicle. For example, identification of curbs 920provides information that helps to keep the vehicle within the physicalboundaries of road 910, away from pedestrians 930 on the sidewalk.Further, identification of curbs 920 allows system 100 to identifypedestrians that are on a surface of road 910, and thus, identifypotential pedestrian hazards, which may require further action to avoida collision.

The entirety of image 900 may represent a FOV of one or more of imagecapture devices 122, 124, 126. In some embodiments, system 100 may beconfigured to identify a region of interest (ROI) 950 within the FOV. Inan exemplary embodiment, system 100 may be a detection system configuredto process a selected ROI 950 and/or a full FOV associated with image900 (and/or a plurality of images of which image 900 is one) to identifya curb 920.

In one embodiment, system 100 (e.g., curb identification module 810) maybe configured to detect linear image edges to serve as curb edge linecandidates. Curb identification module 810 may analyze an image toidentify one or more edges lines of a curb, based on the identified curbedge line candidates. For example, curb identification module 810 mayidentify a bottom edge of a curb, which may be a transition line betweenthe curb and the road surface in the image, by analyzing the edge linecandidates to determine which, if any, include characteristics thatmatch that of a curb bottom edge.

In some embodiments, system 100 (e.g., curb registration module 820) maybe configured to determine whether the curb edge line candidate is partof a curb (e.g., the candidate would be correctly identified as an edgeline of a curb) by determining whether a motion field of pixels in thevicinity of the curb edge line candidate is consistent with athree-dimensional structure of a step having an appropriate height. Forexample, system 100 may model a curb as a sharp step function and, usinga plurality of captured images (e.g., a previous and current image),compute what the properties of the step are that would best model themotion field of pixels between the captured image frames. FIG. 9illustrates an example of an identified curb 920A in the vicinity of theclosest-in-path-pedestrian 930A.

In an exemplary embodiment, curb identification module 810 may identifycurb edge line candidates by using an image analysis method. Forexample, system 100 may use a method that utilizes the Hough transform,which is an image processing technique that may be used to identifylines in an image. In another example, system 100 may use the Canny edgedetection technique to identify edges present in an image using imagegradient analysis. Other image analysis methods may be used, such asother methods that group image pixels by intensity, contrast, or otherparameter to identify lines and/or curves present an image.

Curb edges, including the curb bottom edge, commonly appear as anelongated linear edge in the intensity pattern of an image, thusallowing them to be identified using one of the above described imageanalysis methods. However, this is also true for non-curb objects androad marks containing long linear features appearing in the image.Therefore, an elongated linear edge found in an image is not necessarilyan edge of a curb. Curb registration module 820 may perform a curbdetection process to analyze curb edge line candidates to identify acurb edge lines (and therefore the curb).

FIG. 10A depicts an exemplary image 1000, including linear edges 1010identified by curb identification module 810 as potential edges of acurb 1020. Some of linear edges 1010 may correspond to edge lines ofcurb 1020 (e.g., top and/or bottom edges of the curb), while some otherlinear edges 1010 may correspond to non-curb objects, such as cracks,seams, markings, etc. Curb registration module 820 may analyze image1000 and linear edges 1010 to determine the location of and/or modelcurb 1020. FIG. 10B depicts image 1000 with curb 1020 having beendetermined and modeled according to disclosed embodiments.

Curb registration module 820 may be configured to store instructionsassociated with one or more algorithms that allow curb 1020 to beidentified and modeled as shown in FIG. 10B. For example, curbregistration module 820 may include a curb analysis algorithm (CAA) thatmay allow system 100 to identify a curb within an image and/or pluralityof images in a video sequence. In an exemplary embodiment, processingunit 110 may use the CAA to determine whether a curb edge line candidatecorresponds to a curb edge line (e.g., a curb bottom edge or a curb topedge).

FIG. 11 depicts an example of a modeled step function 1100 which may beused to model a curb that corresponds to a real curb 1105, including atleast some parameters that may be used by system 100 when executing theCAA. As shown in FIG. 11, system 110 may analyze a region of interest1110 across multiple captured image frames to model step function 1100,while a mask region 1120 is not considered. This may allow processingunit 110 to focus only on pixels around a curb edge line candidate. Inan exemplary embodiment, in order to determine whether a curb edge linecandidate corresponds to a curb edge line, a height (hereinafter“Z_(curb)”) of step function 1100 (i.e., a height of an associatedmodeled curb) may be calculated. For example, Z_(curb) may be calculatedfor a given curb edge line candidate detected in one image of a videosequence, the previous frame, and known, assumed, or estimatedparameters (e.g., the camera's calibration and ego-motion parameters).

In one example, curb registration module 820 may utilize a parametricimage warp function {tilde over (W)}(x; y; p) and an error minimizationfunction E(p) to calculate Z_(curb) for a given curb edge linecandidate. The image warp function {tilde over (W)}(x; y; p) may be apiecewise homography warp whereby each homography operates on differentportions of a region of interest 1110 to match pixels inthree-dimensional space between captured frame images. For example, theimage warp function W(x; y; p) may be defined as:

${\overset{\sim}{W}\left( {x,{y;p}} \right)} = \begin{Bmatrix}{H_{1}\left( {x,y,1} \right)}^{T} & {{\left( {{\overset{\rightarrow}{x}}_{2} - {\overset{\rightarrow}{x}}_{1}} \right)^{T}\left( {\left( {x,y} \right)^{T} - {\overset{\rightarrow}{x}}_{1}} \right)} < 0} \\{H_{3}\left( {x,y,1} \right)}^{T} & {{\left( {{\overset{\rightarrow}{x}}_{4} - {\overset{\rightarrow}{x}}_{3}} \right)^{T}\left( {\left( {x,y} \right)^{T} - {\overset{\rightarrow}{x}}_{3}} \right)} < 0} \\{H_{3}\left( {x,y,1} \right)}^{T} & {otherwise}\end{Bmatrix}$

Each homography H₁, H₂, and H₃ may correspond to a portion of region ofinterest 1120, as shown in FIG. 11. In an exemplary embodiment, H₁ mayrepresent a region inside of the bottom edge of the curb 1105 (e.g., theroad surface), H₂ may represent a region between the bottom edge of thecurb 1105 and the top edge of the curb 1105 (e.g., the height of thecurb), and H₃ may represent a region that extends away from the top ofthe curb 1105 and the road surface (e.g., the sidewalk), all withinregion of interest 1120. System 100 may use the image warp function{tilde over (W)}(x; y; p) to follow pixels between a plurality ofimages, such as a current image I_(a) and a previous image I_(b), todetermine the step function associated with the curb edge linecandidate.

In an exemplary embodiment, each homography H₁, H₂, and H₃ may becalculated based on a homography determination equation, such as:

$H_{i} = {{K\left( {R_{ab} - \frac{n_{i}t_{ab}^{T}}{d_{i}}} \right)}K^{- 1}}$

In general, the parameters that define each homography may be known,assumed, or estimated based on information associated with vehicle 200and an image capture device 122, 124, 126. For example, K may be animage capturing device parameter which may take into account focallengths, image centers, and/or other camera settings, and may be definedas:

$K = \begin{bmatrix}{fx} & 0 & {xo} \\o & {fy} & {yo} \\0 & 0 & 1\end{bmatrix}$

The inter-frame ego-motion parameters may be found using:t _(ab)=(ΔX,ΔY,ΔZ)^(T)Δθ_(ab)=(Δα,Δβ,Δγ)^(T)

R_(ab)=R_(z)(Δγ)R_(y)(Δβ)R_(x)(Δα) may be the camera rotation matrixcomposed of pitch (x), yaw (y), and roll (z) rotations (for camera pitchα, yaw β, and roll γ angles relative to the road plane), between framesI_(a) and I_(b) in the camera coordinate system of frame I_(a).

Regarding the surface normals n_(i):n ₁ =n ₃ =R _(calib) {circumflex over (Z)}=R _(calib)(0,0,1)^(T)n ₂ =−u×n ₁

In these equations, u=(X₂−X₁)/∥(X₂−X₁)∥, u being a unit vector in thedirection of the curb's bottom edge, and R_(calib)=Rz(γ)Ry(β)Rx(α),R_(calib) being a road calibration matrix.

Regarding the distances to the three planes d_(i):d₁=Z_(cam)d ₂ =|X ₁ ^(T) n ₂|d ₃ =Z _(cam) −Z _(curb)

In the above equations, the world coordinates X₁ and X₂ of the imagecoordinates {right arrow over (x)}₁ and {right arrow over (x)}₂projected onto the road plane may be used. The image coordinates {rightarrow over (x)}₃ and {right arrow over (x)}₄ of the points correspondingto {right arrow over (x)}₁ and {right arrow over (x)}₂ on the upper partof the step function 1100 may define the line separating regions H₂ andH₃ within the region of interest 1110. These points may thus be defined:

$X_{1,2} = \frac{d_{1}K^{- 1}{\overset{\sim}{x}}_{1,2}}{n_{1}^{T}K^{- 1}{\overset{\sim}{x}}_{1,2}}$X_(3, 4) = X_(1, 2) + Z_(curb)n₁${\overset{\sim}{x}}_{3,4} = {KX}_{3,4}$

In these equations, x may be used to denote the three-dimensionalhomogenous image coordinates of the corresponding two-dimensional imagepoint x.

The above equations may be used to determine the homographies H1, H2,and H3 using the image warp function {tilde over (W)}(x; y; p). Sincethe curb height Z_(curb) for step function 1100 is an unknown parameter,the image warp function {tilde over (W)}(x; y; p) may be iterativelysolved until an appropriate Z_(curb) is determined. The appropriateZ_(curb) may correspond to a result of the image warp equation {tildeover (W)}(x; y; p) that is within a satisfactory error when warping thecoordinates of the region of interest 1120 between captured imageframes. This may be determined using the error minimization functionE(p), where E(p) is defined as:

${E(p)} = {\sum\limits_{{({x,y})} \in {C\backslash M}}\;\left\lbrack {{{Ib}\left( {\left\lbrack {x,y} \right\rbrack T} \right)} - {{Ia}\left( {\overset{\rightarrow}{W}\left( {x,{y;p}} \right)} \right)}} \right\rbrack^{2}}$

The error minimization function E(p) may be used to minimize adifference between a current frame and the warp of a previous frameusing known information, assumptions, and or estimated information. Forexample, system 100 may use the brightness constancy assumption whenusing the error minimization function E(p). The error minimizationfunction E(p) allows one captured image frame to be compared to anothercaptured image frame by warping the coordinates of one frame to thecoordinates of the other frame. Minimizing the error of the warp allowssystem 100 to provide a more accurate calculation of Z_(curb) (e.g.,because a low error value may indicate that the difference betweencaptured image frame is accurately considered).

For each curb edge line candidate, system 100 may use the image warpfunction {tilde over (W)}(x; y; p) to identify the parameter vector p.The parameter vector p includes at least Z_(curb), depending on whetherother parameters are known, assumed, or estimated (e.g., additionaldegrees of freedom may be built in to allow some parameters to beunknown). Based on the parameter vector p, system 100 may determine acurb height Z_(curb) that is within an error threshold determined basedon the error minimization function E(p). This Z_(curb) value mayrepresent a value of a height of a step function modeled to a given curbedge line candidate. System 100 may compare the calculated Z_(curb)value to an expected Z_(curb) value (e.g., an average height of a curbthat may be expected to be found on the road) to determine whether thecalculated Z_(curb) value is within a threshold difference. If thecalculated Z_(curb) value is within the threshold difference, system 100may determine that the curb edge line candidate corresponds to a curbedge line.

System 100 may repeat the process of using the CAA (which may includethe equations described above) to determine whether one or moreidentified curb edge line candidate corresponds to an curb edge line.System 100 may model each curb edge line candidate as both a curb bottomedge and a curb top edge to determine whether the curb edge linecandidate matches either edge of a curb. Based on these processes,system 100 may determine the location of one or more curbs within animage or video sequence of images.

After a curb has been identified, pedestrian identification module 830may perform one or more processes to identify a pedestrian relative tothe curb. For example, pedestrian identification module 830 may assesswhether an identified pedestrian is a potential hazard. System 100 mayuse the information related to the curb and the pedestrians to providewarnings to a driver and/or control the vehicle as it travels on theroad.

FIG. 12 is a flowchart showing an exemplary process 1200 for identifyinga curb within an image and/or video sequence. In an exemplaryembodiment, system 100 may perform process 1200 to determine thelocation of a curb such that system 100 may determine a boundary of aroad surface. Knowledge of the boundary of the road surface may allowsystem 100 to promote safe operation of a vehicle, through helping tomaintain the vehicle on the road surface and out of collisions withobjects and/or pedestrians. Further, knowledge of the boundary of theroad surface may allow system 100 to assess whether any pedestrians orother objects are on the road surface and therefor hazards for whichsome action to prevent a collision should be taken.

In step 1202, processing unit 110 may receive a plurality of images of avehicle environment. For example, one or more of image capture devices122, 124, 126 may acquire a plurality of images of an area forward ofvehicle 200. The area forward of the vehicle may include variousfeatures, such as those depicted in FIG. 9.

In step 1204, processing unit 110 may identify curb edge linecandidates. For example, processing unit 110 (e.g., curb identificationmodule 810) may use an algorithm or equation to identify linear edgeswithin a capture images. These linear edges may represent curb edgecandidates that may be further processed to determine whether theycorrectly represent edges of an curb present in the image or videosequence. In one example, processing unit 110 may use an edge detectionmethod, such as using the Hough transform or by fitting lines to longconnected components of the Canny edge response, to identify curb edgeline candidates.

In step 1206, processing unit 110 may analyze each curb edge linecandidate. For example, processing unit 110 (e.g., curb registrationmodule 820) may use an algorithm or equation to iteratively process curbedge line candidates to determine whether any of them correspond to acurb edge line. For example, processing unit may use the CAA to modeleach curb edge line candidate as a step function and determine whether acorresponding height of that step function corresponds to what would beexpected of a real curb.

In step 1208, processing unit 110 may identify at least one of the curbedge line candidates as an edge line of a curb. For example, processingunit 110 (e.g., curb registration module 820) may select a curb edgeline candidate that is determined to be an edge line of a curb and storeor otherwise identify that edge line candidate as a curb edge line. Thecurb edge line may be identified as a curb top edge or a curb bottomedge.

In addition, processing unit 110 may identify regions that correspond tothe road surface and the off-road surface based on the identified edgeline of the curb. For example, processing unit 110 may use determinededge lines of the curb to identify the curb throughout an image and/orvideo sequence and may identify a first region as a road surface and asecond region as an off-road surface, throughout the image and/or eachimage of a video sequence. In this way, processing unit 110 may identifya physical boundary of a road on which vehicle 200 travels. In someembodiments, processing unit 110 may store location and/or coordinateinformation associated with the identified curb edge line or curb.

In step 1210, processing unit 110 may use the identified curb edge lineor curb to control vehicle 200. For example, processing unit 110 mayprovide location and/or coordinate information to a vehicle controlmodule that makes decisions regarding speed, direction, acceleration,etc., of vehicle 200. In some instances, processing unit 110 may use themodeled curb to assess whether there are hazards that need to beconsidered. For example, processing unit 110 may use a modeled curb toassess pedestrian hazards, as explained in more detail below.

FIG. 13 is a flowchart showing an exemplary process 1300 for assessinghazards presented by pedestrians or other objects that may be locatedwithin a roadway. In an exemplary embodiment, system 100 may performprocess 1300 to determine the location on one or more pedestrians in aFOV of an image capturing device 122, 124, 126. For example, system 100may determine that one or more pedestrians is located on a road surfaceof a road (e.g., and therefore not off-road) and determine one or moreactions that should be taken to prevent a collision with the pedestrian.

In step 1310, processing unit 110 may identify pedestrians within animage and/or images of a video sequence. For example, processing unit110 (e.g., pedestrian identification module 830) may process one or moreimages to identify objects of interest within the images. Processingunit 110 may further analyze the objects of interest to determinewhether they correspond to a pedestrian (or other object which may betreated as a pedestrian, such as an animal, bicyclist, etc.). In oneexample, processing unit may compare an image to stored images to matchcharacteristics of an image with information that is known to correspondto a pedestrian (e.g., a figure in the shape of a person). In anotherexample, processing unit may compare multiple images of a video sequenceto identify objects that are moving in a manner consistent with apedestrian.

In step 1320, processing unit 110 may classify pedestrians. For example,processing unit 110 (e.g., pedestrian identification module 830) mayclassify pedestrians as either on the road surface or on the off-roadsurface. In one example, processing unit 110 may compare the locationand/or trajectory of an identified pedestrian with the location of anidentified and modeled curb (e.g., a curb modeled using process 1200).For instance, processing unit may determine whether the pedestrian iswithin a region that is determined to be the road surface (e.g., insideof a bottom edge of the curb) or within a region that is determined tobe the off-road surface (e.g., outside of a top edge of the curb).

In step 1330, processing unit 110 may assess whether a pedestrianpresents a hazard to vehicle 200. In one example, processing unit 110may determine that a pedestrian on the road surface is a hazard. Inanother example, processing unit 110 may compare a trajectory of vehicle200 to a trajectory of an identified pedestrian on the road surface todetermine whether there is a possibility of a collision between vehicleand the pedestrian.

In step 1340, processing unit 110 may alert a driver and/or controlvehicle 200 to prevent a collision. In one example, processing unit 110may alert a driver to a pedestrian on a road surface whenever apedestrian is classified as being on the road surface. In anotherexample, processing unit 110 may alert a driver only if processing unit110 determines that a collision with the pedestrian is likely (e.g.,above a risk threshold, such as based on a predicted closest distancebetween vehicle 200 and the pedestrian at some point in time). In someembodiments, processing unit 110 may use the assessment of a pedestrianhazard to automatically control travel of vehicle 200. For example,processing unit 110 may modify a path (e.g., steering direction), speed,acceleration, etc., of vehicle 200 in order to avoid a collision with apedestrian.

System 100 may use processes 1200 and/or 1300 to identify features of anenvironment of vehicle 200, and use those features to operate/controlvehicle 200. In particular, as discussed in the examples above, system100 may identify a road boundary and determine whether there are anypedestrian hazards based on their location with respect to the roadboundary. It should be understood, however, that these are examples, andthat other similar processes may be implemented. For example, other roadboundaries besides curbs, such as railings, medians, shoulders, etc.,may be determined, and other hazards besides pedestrians, such as othervehicles, debris, road imperfections, etc., may be identified andassessed based on their location and/or position with respect to a roadboundary.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, or other opticaldrive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A detection system, the system comprising: atleast one processing device programmed to: receive a plurality of imagesof an area output from at least one camera via a data interface;determine a plurality of road edge line candidates from and within theplurality of images of the area; and identify, based on the road edgeline candidates, a region in the plurality of images that corresponds toa road surface and a region in the plurality of images that correspondsto an off-road surface.
 2. The detection system of claim 1, wherein theat least one processing device is further programmed to: identify apedestrian in one or more of the plurality of images; assess whether thepedestrian presents a hazard; and determine an action to be taken toavoid a collision with the pedestrian, based on the assessment.
 3. Thedetection system of claim 2, wherein assessing whether the pedestrianpresents a hazard includes classifying the pedestrian based on theidentified regions and at least one of a location or a trajectory of thepedestrian.
 4. The detection system of claim 3, wherein classifying thepedestrian includes classifying the pedestrian as on the road surface oron the off-road surface.
 5. The detection system of claim 2, whereinassessing whether the pedestrian presents a hazard includes comparing atrajectory of the pedestrian to a trajectory of a vehicle.
 6. Thedetection system of claim 2, wherein the determined action to be takento avoid the collision includes providing an alert to a driver.
 7. Thedetection system of claim 2, wherein the determined action to be takento avoid the collision includes modifying at least one of a speed,acceleration, or direction of a vehicle.
 8. The detection system ofclaim 1, wherein the at least one processing device is furtherprogrammed to: identify an object in one or more of the plurality ofimages; assess whether the object presents a hazard; and determine anaction to be taken to avoid a collision with the object, based on theassessment.
 9. The detection system of claim 8, wherein assessingwhether the object presents a hazard includes classifying the objectbased on the identified regions and at least one of a location or atrajectory of the object.
 10. The detection system of claim 9, whereinclassifying the object includes classifying the object as on the roadsurface or on the off-road surface.
 11. A vehicle, comprising: a body;and at least one processing device programmed to: receive a plurality ofimages of an area output from at least one camera via a data interface;determine a plurality of road edge line candidates from and within theplurality of images of the area; and identify, based on the road edgeline candidates, a region in the plurality of images that corresponds toa road surface and a region in the plurality of images that correspondsto an off-road surface.
 12. The vehicle of claim 11, wherein the atleast one processing device is further programmed to: identify apedestrian in one or more of the plurality of images; assess whether thepedestrian presents a hazard; and determine an action to be taken toavoid a collision with the pedestrian, based on the assessment.
 13. Thevehicle of claim 12, wherein assessing whether the pedestrian presents ahazard includes classifying the pedestrian based on the identifiedregions and at least one of a location or a trajectory of thepedestrian.
 14. The vehicle of claim 13, wherein classifying thepedestrian includes classifying the pedestrian as on the road surface oron the off-road surface.
 15. The vehicle of claim 12, wherein assessingwhether the pedestrian presents a hazard includes comparing a trajectoryof the pedestrian to a trajectory of a vehicle.
 16. The vehicle of claim12, wherein the determined action to be taken to avoid the collisionincludes providing an alert to a driver.
 17. The vehicle of claim 12,wherein the determined action to be taken to avoid the collisionincludes modifying at least one of a speed, acceleration, or directionof a vehicle.
 18. The vehicle of claim 11, wherein the at least oneprocessing device is further programmed to: identify an object in one ormore of the plurality of images; assess whether the object presents ahazard; and determine an action to be taken to avoid a collision withthe object, based on the assessment.
 19. The vehicle of claim 18,wherein assessing whether the object presents a hazard includesclassifying the object based on the identified regions and at least oneof a location or a trajectory of the object, and classifying the objectincludes classifying the object as on the road surface or on theoff-road surface.
 20. A method for hazard assessment, the methodcomprising the following operations performed by one or more processors:receiving a plurality of images of an area output from at least onecamera via a data interface; determining a plurality of road edge linecandidates from and within the plurality of images of the area; andidentifying, based on the road edge line candidates, a region in theplurality of images that corresponds to a road surface and a region inthe plurality of images that corresponds to an off-road surface.